David Snelling
8ff382ca3b
CRITICAL CHECKPOINT - DO NOT PUSH TO GITHUB Recovery Status: - Successfully recovered brainy.ts from compiled JavaScript - All core v3.0 API methods functional (add, get, update, delete, relate, find, etc.) - Neural subsystem intact (562KB embedded patterns, NLP working) - Augmentation pipeline operational (20+ augmentations) - HNSW clustering system complete - Triple Intelligence compiled (needs constructor fix) - Test suite validates functionality Changes preserved: - 898 files with changes from last 3 days - 144,475 insertions - All augmentation improvements - All test coverage enhancements - Complete v3.0 feature set This is a LOCAL checkpoint only - contains recovered work after corruption incident. Created backup in .backups/brainy-full-20250910-151314.tar.gz Branch: recovery-checkpoint-20250910-151433 Date: Wed Sep 10 03:18:04 PM PDT 2025
100 lines
No EOL
4 KiB
JavaScript
100 lines
No EOL
4 KiB
JavaScript
#!/usr/bin/env node
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/**
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* 🧠 Pre-compute Pattern Embeddings Script
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*
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* This script pre-computes embeddings for all patterns and saves them to disk.
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* Run this once after adding new patterns to avoid runtime embedding costs.
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*
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* How it works:
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* 1. Load all patterns from library.json
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* 2. Use Brainy's embedding model to encode each pattern's examples
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* 3. Average the example embeddings to get a robust pattern representation
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* 4. Save embeddings to patterns/embeddings.bin for instant loading
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*
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* Benefits:
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* - Pattern matching becomes pure math (cosine similarity)
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* - No embedding model calls during query processing
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* - Patterns load instantly with pre-computed vectors
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*/
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import { BrainyData } from '../brainyData.js';
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import patternData from '../patterns/library.json' assert { type: 'json' };
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import * as fs from 'fs/promises';
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import * as path from 'path';
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async function precomputeEmbeddings() {
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console.log('🧠 Pre-computing pattern embeddings...');
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// Initialize Brainy with minimal config
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const brain = new BrainyData({
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storage: { forceMemoryStorage: true },
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logging: { verbose: false }
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});
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await brain.init();
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console.log('✅ Brainy initialized');
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const embeddings = {};
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let processedCount = 0;
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const totalPatterns = patternData.patterns.length;
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for (const pattern of patternData.patterns) {
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console.log(`\n📝 Processing pattern: ${pattern.id} (${++processedCount}/${totalPatterns})`);
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console.log(` Category: ${pattern.category}`);
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console.log(` Examples: ${pattern.examples.length}`);
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// Embed all examples
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const exampleEmbeddings = [];
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for (const example of pattern.examples) {
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try {
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const embedding = await brain.embed(example);
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exampleEmbeddings.push(embedding);
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console.log(` ✓ Embedded: "${example.substring(0, 50)}..."`);
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}
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catch (error) {
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console.error(` ✗ Failed to embed: "${example}"`, error);
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}
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}
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if (exampleEmbeddings.length === 0) {
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console.warn(` ⚠️ No embeddings generated for pattern ${pattern.id}`);
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continue;
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}
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// Average the embeddings for a robust representation
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const avgEmbedding = averageVectors(exampleEmbeddings);
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embeddings[pattern.id] = {
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patternId: pattern.id,
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embedding: avgEmbedding,
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examples: pattern.examples,
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averageMethod: 'arithmetic_mean'
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};
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console.log(` ✅ Generated ${avgEmbedding.length}-dimensional embedding`);
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}
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// Save embeddings to file
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const outputPath = path.join(process.cwd(), 'src', 'patterns', 'embeddings.json');
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await fs.writeFile(outputPath, JSON.stringify(embeddings, null, 2));
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console.log(`\n✅ Saved ${Object.keys(embeddings).length} pattern embeddings to ${outputPath}`);
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// Calculate storage size
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const stats = await fs.stat(outputPath);
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console.log(`📊 File size: ${(stats.size / 1024).toFixed(2)} KB`);
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// Print statistics
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console.log('\n📈 Embedding Statistics:');
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console.log(` Total patterns: ${totalPatterns}`);
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console.log(` Successfully embedded: ${Object.keys(embeddings).length}`);
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console.log(` Failed: ${totalPatterns - Object.keys(embeddings).length}`);
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console.log(` Embedding dimensions: ${Object.values(embeddings)[0]?.embedding.length || 0}`);
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await brain.close();
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console.log('\n✅ Complete!');
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}
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function averageVectors(vectors) {
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if (vectors.length === 0)
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return [];
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const dim = vectors[0].length;
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const avg = new Array(dim).fill(0);
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// Sum all vectors
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for (const vec of vectors) {
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for (let i = 0; i < dim; i++) {
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avg[i] += vec[i];
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}
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}
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// Divide by count to get average
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for (let i = 0; i < dim; i++) {
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avg[i] /= vectors.length;
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}
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return avg;
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}
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// Run the script
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precomputeEmbeddings().catch(console.error);
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//# sourceMappingURL=precomputePatternEmbeddings.js.map
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